Friedel M.J.,Crustal Geophysics and Geochemistry Science Center |
Friedel M.J.,University of Colorado at Denver
Applied Soft Computing Journal | Year: 2013
Few studies attempt to model the economic feasibility of mining undiscovered mineral resources given the sparseness of data; and the coupled, nonlinear, spatial, and temporal relationships among variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 203 porphyry copper deposit sites across the world. The sparse data set includes one dependent variable indicating the economic feasibility, and seventy two independent variables from categories describing characteristics of mining method, metallurgy, dimensions, economics, and amount. Analysis of component planes reveals relations and strengths in the underlying SOM multivariate density function which are used to impute missing values. Application of the Davies-Bouldin criteria to k-means clusters of SOM neurons identified 14 regional economic resource units (conceptual models). A best subsets approach applied to median values from these models identified 20 statistically significant combinations of variables. During model fitting by the multiple linear regression technique, only four of the empirical models had variables that were all significant at the 95% confidence level. The best model explained 98% of the variability in economic feasibility and incorporated variables describing distance to natural gas, road, and water; and the total amount of resources. This model was independently validated by comparing predictions of economic feasibility at 68 mine sites not included in the training data. Eighty-four percent of the reported economic feasibility is correctly predicted with 8 false positives and 2 false negative. We demonstrate the application of this model to a permissive copper porphyry tract that crosses a portion of British Columbia and Yukon territories of Canada. The proposed hybrid approach provides an alternative modeling paradigm for translating estimates of contained metal into meaningful societal measures. © Published by Elsevier B.V.
Friedel M.J.,Crustal Geophysics and Geochemistry Science Center
Environmental Modelling and Software | Year: 2011
Few studies attempt to model the range of possible post-fire hydrologic and geomorphic hazards because of the sparseness of data and the coupled, nonlinear, spatial, and temporal relationships among landscape variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 540 burned basins in the western United States. The sparsely populated data set includes variables from independent numerical landscape categories (climate, land surface form, geologic texture, and post-fire condition), independent landscape classes (bedrock geology and state), and dependent initiation processes (runoff, landslide, and runoff and landslide combination) and responses (debris flows, floods, and no events). Pattern analysis of the SOM-based component planes is used to identify and interpret relations among the variables. Application of the Davies-Bouldin criteria following k-means clustering of the SOM neurons identified eight conceptual regional models for focusing future research and empirical model development. A split-sample validation on 60 independent basins (not included in the training) indicates that simultaneous predictions of initiation process and response types are at least 78% accurate. As climate shifts from wet to dry conditions, forecasts across the burned landscape reveal a decreasing trend in the total number of debris flow, flood, and runoff events with considerable variability among individual basins. These findings suggest the SOM may be useful in forecasting real-time post-fire hazards, and long-term post-recovery processes and effects of climate change scenarios. © 2011.
Todorov T.I.,Crustal Geophysics and Geochemistry Science Center |
Ejnik J.W.,University of Wisconsin - Whitewater |
Guandalini G.,The Joint Pathology Center |
Xu H.,The Joint Pathology Center |
And 5 more authors.
Journal of Trace Elements in Medicine and Biology | Year: 2013
In this study we report uranium analysis for human semen samples. Uranium quantification was performed by inductively coupled plasma mass spectrometry. No additives, such as chymotrypsin or bovine serum albumin, were used for semen liquefaction, as they showed significant uranium content. For method validation we spiked 2. g aliquots of pooled control semen at three different levels of uranium: low at 5. pg/g, medium at 50. pg/g, and high at 1000. pg/g. The detection limit was determined to be 0.8. pg/g uranium in human semen. The data reproduced within 1.4-7% RSD and spike recoveries were 97-100%. The uranium level of the unspiked, pooled control semen was 2.9. pg/g of semen (. n=. 10). In addition six semen samples from a cohort of Veterans exposed to depleted uranium (DU) in the 1991 Gulf War were analyzed with no knowledge of their exposure history. Uranium levels in the Veterans' semen samples ranged from undetectable (<0.8. pg/g) to 3350. pg/g. This wide concentration range for uranium in semen is consistent with known differences in current DU body burdens in these individuals, some of whom have retained embedded DU fragments. © 2012.
Akbari Esfahani A.,University of Colorado at Denver |
Akbari Esfahani A.,Crustal Geophysics and Geochemistry Science Center |
Friedel M.J.,University of Colorado at Denver |
Friedel M.J.,Crustal Geophysics and Geochemistry Science Center
Environmental Modelling and Software | Year: 2014
A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009-2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists. © 2013.